ConicCat/Nemo-super-wip-lora

See axolotl config

axolotl version: 0.16.0.dev0

base_model: nvidia/Llama-3_3-Nemotron-Super-49B-v1_5


load_in_8bit: true
load_in_4bit: false

sequence_len: 5120
max_sample_length: 5120

sample_packing: true
gradient_checkpointing: true

bf16: true
tf32: true

flash_attention: true
lora_mlp_kernel: false
lora_qkv_kernel: false
lora_o_kernel: false


datasets:
  - path: ConicCat/GLiMA_Thinking
    type: chat_template
    roles_to_train: []
    train_on_eos: turn
    message_field_training: train

  - path: ConicCat/Gutenberg-SFT
    type: chat_template

  - path: ConicCat/Condor-SFT-Filtered
    split: train[:250]
    type: chat_template

  - path: ConicCat/Ao3_Soft_Refusal
    type: chat_template

  - path: ConicCat/VSF
    type: chat_template

chat_template_jinja: "{% set bos = \"<|begin_of_text|>\" %}{%- set enable_thinking = false -%}{% set system_start_header = \"<|start_header_id|>\" %}{% set system_end_header = \"<|end_header_id|>\n\n\" %}{% set start_header = \"<|start_header_id|>\" %}{% set end_header = \"<|end_header_id|>\n\n\" %}{% set eot = \"<|eot_id|>\" %}{% set system_token = \"system\" %}{% set user_token = \"user\" %}{% set assistant_token = \"assistant\" %}{% set tool_token = \"tool\" %}{{- bos ~ system_start_header ~ system_token ~ system_end_header -}}{%- if messages[0].role == 'system' and messages[0].content != '' -%}{%- set system_content = messages[0].content -%}{%- if '/no_think' in system_content -%}{%- set system_content = system_content.replace('/no_think', '')|trim -%}{%- set enable_thinking = false -%}{%- elif '/think' in system_content -%}{%- set system_content = system_content.replace('/think', '')|trim -%}{%- set enable_thinking = true -%}{%- endif -%}{{- system_content + '\n\n' -}}{%- endif -%}{%- if tools -%}{{- 'You can use the following tools to assist the user if required:\n<AVAILABLE_TOOLS>[' -}}{%- for tool in tools -%}{{- (tool.function if tool.function is defined else tool) | tojson -}}{{- ', ' if not loop.last else '' -}}{%- endfor -%}{{- ']</AVAILABLE_TOOLS>\n\nIf you decide to call any tool(s), use the following format:\n<TOOLCALL>[{{\"name\": \"tool_name1\", \"arguments\": \"tool_args1\"}}, {{\"name\": \"tool_name2\", \"arguments\": \"tool_args2\"}}]</TOOLCALL>\n\nResponse from tool(s) will be returned in this format:\n<TOOL_RESPONSE>[{{\"response\": \"tool_response1\"}}, {{\"response\": \"tool_response2\"}}]</TOOL_RESPONSE>\n\nBased on the results returned by the tool(s), you can call additional tools if needed, correct tool calls if any errors are found, or just respond with the answer to the user.' -}}{%- endif -%}{{- eot -}}{%- for message in messages -%}{%- if message.role == user_token -%}{{- start_header ~ user_token ~ end_header -}}{{ message.content -}}{{ eot -}}{%- elif message.role == assistant_token -%}{%- if '</think>' in message.content -%}{%- set content = message.content.split('</think>')[-1].lstrip() -%}{%- else -%}{%- set content = message.content -%}{%- endif -%}{{- start_header ~ assistant_token ~ end_header -}}{{ content -}}{%- if message.tool_calls -%}{{- '<TOOLCALL>[' -}}{%- for call in message.tool_calls -%}{%- set fn = call.function if call.function is defined else call -%}{{- '{\"name\": \"' + fn.name + '\", \"arguments\": ' -}}{%- if fn.arguments is string -%}{{- fn.arguments -}}{%- else -%}{{- fn.arguments | tojson -}}{%- endif -%}{{- '}' + (', ' if not loop.last else '') -}}{%- endfor -%}{{- ']</TOOLCALL>' -}}{%- endif -%}{{- eot -}}{%- elif message.role == tool_token -%}{%- if loop.first or (messages[loop.index0 - 1].role != tool_token) -%}{{- start_header ~ tool_token ~ end_header -}}{{ '<TOOL_RESPONSE>[' -}}{%- endif -%}{{- message.content -}}{{- ', ' if not loop.last and (messages[loop.index0 + 1].role == tool_token) else '' -}}{%- if loop.last or (messages[loop.index0 + 1].role != tool_token) -%}{{- ']</TOOL_RESPONSE>' -}}{{ eot -}}{%- endif -%}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{- start_header ~ assistant_token ~ end_header -}}{%- if not enable_thinking -%}{{- '<think>\n\n</think>\n\n' -}}{%- endif -%}{%- endif -%}"
trust_remote_code: true

adapter: lora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.0
lora_bias: None
lora_target_linear: true
use_tensorboard: true

optimizer: paged_adamw_8bit
learning_rate: 1.25e-5 # 1e-4 / 4
loraplus_lr_ratio: 16

# Training arguments
output_dir: ./Writer-Stage-1
num_epochs: 3
micro_batch_size: 1
gradient_accumulation_steps: 16
save_strategy: 'no'
warmup_ratio: 0.05
lr_scheduler: 'constant_with_warmup'
max_grad_norm: 1
logging_steps: 1
seed: 42

Writer-Stage-1

This model is a fine-tuned version of nvidia/Llama-3_3-Nemotron-Super-49B-v1_5 on the ConicCat/GLiMA_Thinking, the ConicCat/Gutenberg-SFT, the ConicCat/Condor-SFT-Filtered, the ConicCat/Ao3_Soft_Refusal and the ConicCat/VSF datasets.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1.25e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: constant_with_warmup
• lr_scheduler_warmup_steps: 2
• training_steps: 54

Training results

Framework versions

• PEFT 0.18.1
• Transformers 5.3.0
• Pytorch 2.9.1+cu128
• Datasets 4.5.0
• Tokenizers 0.22.2